jupyter-live-kernel

To maintain Python variable states across multiple code executions, you can use a live Jupyter kernel. This stateful Python REPL preserves variable states between cells, enabling incremental state building and iterative code development without losing previously defined variables.

Yes, running a live Jupyter kernel for iterative data science coding requires both JupyterLab and Python for runtime execution. These dependencies provide the necessary environment to create and modify cells within a Jupyter notebook.

The best way to inspect DataFrames and explore APIs iteratively is by using a stateful Python REPL. A live Jupyter kernel allows you to experiment with code that depends on the state of previously executed commands, building upon steps incrementally.

To start running Python scripts in a live Jupyter kernel, execute the provided Python script that prints the 'pi' value from memory. This quick start step verifies the kernel's stateful execution and interactive environment setup.

You should use a live Jupyter kernel instead of a standard Python script when you need to perform exploratory data science tasks and complex computations. It accommodates building upon and modifying code step by step within a persistent stateful environment.